State-of-the-art neural coreference resolution for chatbots

Coreference is a rather old NLP research topic [1]. It* has seen a revival of interest in the past two years as several research groups [2] applied cutting-edge deep-learning and reinforcement-learning techniques to it. It was published earlier this year that coreference resolution may be instrumental in improving the performances of NLP neural architectures like RNN and LSTM (see "Linguistic Knowledge as Memory for Recurrent Neural Networks" by B. Dhingra, Z. Yang, W. W. Cohen, and R. Salakhutdinov). Traditionally the set of features was hand-engineered from linguistic features and it could be huge. Some high quality systems use 120 features [4]! Here comes the nice thing about modern NLP techniques like word vectors and neural nets.